MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation

John Lambert, Zhuang Liu, Ozan Sener, James Hays, Vladlen Koltun
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
Input image Ground truth ADE20K model Mapillary model COCO model MSeg model Figure 1: MSeg unifies multiple semantic segmentation datasets by reconciling their taxonomies and resolving incompatible annotations. This enables training models that perform consistently across domains and generalize better. Input images in this figure were taken (top to bottom) from the ScanNet [8], WildDash [44], and Pascal VOC [10] datasets, none of which were seen during training.
doi:10.1109/cvpr42600.2020.00295 dblp:conf/cvpr/LambertLSHK20 fatcat:nxepr7qrwbgqzhcsuiqlp7c7oi